






'''simple docstring'''





from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
_UpperCamelCase						:						int  =					200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
_UpperCamelCase						:						List[Any]  =					50
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
_UpperCamelCase						:						int  =					0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1_000))
def __UpperCAmelCase   (					A					:			str			,       A					:			str		)    ->   tuple[str, float]:
			UpperCAmelCase_      :       Union[str, Any]          =       len([g for position, g in enumerate(A		) if g == main_target[position]]		)
			return (item, float(A		))
def __UpperCAmelCase   (					A					:			str			,       A					:			str		)    ->   tuple[str, str]:
			UpperCAmelCase_      :       Optional[int]          =       random.randint(0			,       len(A		) - 1		)
			UpperCAmelCase_      :       str          =       parent_a[:random_slice] + parent_a[random_slice:]
			UpperCAmelCase_      :       Dict          =       parent_a[:random_slice] + parent_a[random_slice:]
			return (child_a, child_a)
def __UpperCAmelCase   (					A					:			str			,       A					:			list[str]		)    ->   str:
			UpperCAmelCase_      :       List[Any]          =       list(A		)
			if random.uniform(0			,       1		) < MUTATION_PROBABILITY:
						UpperCAmelCase_      :       Dict          =       random.choice(A		)
			return "".join(A		)
def __UpperCAmelCase   (					A					:			tuple[str, float]			,       A					:			list[tuple[str, float]]			,       A					:			list[str]			,       )    ->   list[str]:
			UpperCAmelCase_      :       str          =       []
			# Generate more children proportionally to the fitness score.
			UpperCAmelCase_      :       List[Any]          =       int(parent_a[1] * 1_0_0		) + 1
			UpperCAmelCase_      :       Union[str, Any]          =       1_0 if child_n >= 1_0 else child_n
			for _ in range(A		):
						UpperCAmelCase_      :       List[str]          =       population_score[random.randint(0			,       A		)][0]

						UpperCAmelCase_    ,      UpperCAmelCase_      :       int          =       crossover(parent_a[0]			,       A		)
						# Append new string to the population list.
						pop.append(mutate(A			,       A		)		)
						pop.append(mutate(A			,       A		)		)
			return pop
def __UpperCAmelCase   (					A					:			str			,       A					:			list[str]			,       A					:			bool = True		)    ->   tuple[int, int, str]:

			# Verify if N_POPULATION is bigger than N_SELECTED
			if N_POPULATION < N_SELECTED:
						UpperCAmelCase_      :       List[Any]          =       F"{N_POPULATION} must be bigger than {N_SELECTED}"
						raise ValueError(A		)
			# Verify that the target contains no genes besides the ones inside genes variable.
			UpperCAmelCase_      :       Any          =       sorted({c for c in target if c not in genes}		)
			if not_in_genes_list:
						UpperCAmelCase_      :       Union[str, Any]          =       F"{not_in_genes_list} is not in genes list, evolution cannot converge"
						raise ValueError(A		)

			# Generate random starting population.
			UpperCAmelCase_      :       int          =       []
			for _ in range(A		):
						population.append(''''''.join([random.choice(A		) for i in range(len(A		)		)]		)		)

			# Just some logs to know what the algorithms is doing.
			UpperCAmelCase_    ,      UpperCAmelCase_      :       List[str]          =       0, 0

			# This loop will end when we find a perfect match for our target.
			while True:
						generation += 1
						total_population += len(A		)

						# Random population created. Now it's time to evaluate.

						# Adding a bit of concurrency can make everything faster,
						#
						# import concurrent.futures
						# population_score: list[tuple[str, float]] = []
						# with concurrent.futures.ThreadPoolExecutor(
						#                                   max_workers=NUM_WORKERS) as executor:
						#     futures = {executor.submit(evaluate, item) for item in population}
						#     concurrent.futures.wait(futures)
						#     population_score = [item.result() for item in futures]
						#
						# but with a simple algorithm like this, it will probably be slower.
						# We just need to call evaluate for every item inside the population.
						UpperCAmelCase_      :       Union[str, Any]          =       [evaluate(A			,       A		) for item in population]

						# Check if there is a matching evolution.
						UpperCAmelCase_      :       int          =       sorted(A			,       key=lambda A		: x[1]			,       reverse=A		)
						if population_score[0][0] == target:
									return (generation, total_population, population_score[0][0])

						# Print the best result every 10 generation.
						# Just to know that the algorithm is working.
						if debug and generation % 1_0 == 0:
									print(
									    F"\nGeneration: {generation}"
									    F"\nTotal Population:{total_population}"
									    F"\nBest score: {population_score[0][1]}"
									    F"\nBest string: {population_score[0][0]}"		)

						# Flush the old population, keeping some of the best evolutions.
						# Keeping this avoid regression of evolution.
						UpperCAmelCase_      :       List[str]          =       population[: int(N_POPULATION / 3		)]
						population.clear()
						population.extend(A		)
						# Normalize population score to be between 0 and 1.
						UpperCAmelCase_      :       Dict          =       [
						    (item, score / len(A		)) for item, score in population_score
						]

						# This is selection
						for i in range(A		):
									population.extend(select(population_score[int(A		)]			,       A			,       A		)		)
									# Check if the population has already reached the maximum value and if so,
									# break the cycle.  If this check is disabled, the algorithm will take
									# forever to compute large strings, but will also calculate small strings in
									# a far fewer generations.
									if len(A		) > N_POPULATION:
												break


if __name__ == "__main__":
						_UpperCamelCase						:						List[Any]  =					(
						    'This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!'
						)
						_UpperCamelCase						:						Any  =					list(
						    ' ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm'
						    'nopqrstuvwxyz.,;!?+-*#@^\'èéòà€ù=)(&%$£/\\'
						)
						_UpperCamelCase ,							_UpperCamelCase ,							_UpperCamelCase						:						Tuple  =					basic(target_str, genes_list)
						print(
						    f'''\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}'''
						)










'''simple docstring'''





from unittest.mock import patch

import pyspark

from datasets.packaged_modules.spark.spark import (
    Spark,
    SparkExamplesIterable,
    _generate_iterable_examples,
)

from ..utils import (
    require_dill_gt_0_3_2,
    require_not_windows,
)
def __UpperCAmelCase   (					A					:			Optional[int]			,       A					:			Optional[int]		)    ->   str:
			UpperCAmelCase_      :       List[Any]          =       []
			for part_id in partition_order:
						UpperCAmelCase_      :       Any          =       df.where(F"SPARK_PARTITION_ID() = {part_id}"		).collect()
						for row_idx, row in enumerate(A		):
									expected_row_ids_and_row_dicts.append((F"{part_id}_{row_idx}", row.asDict())		)
			return expected_row_ids_and_row_dicts
@require_not_windows
@require_dill_gt_0_3_2
def __UpperCAmelCase   (					)    ->   Any:
			UpperCAmelCase_      :       Any          =       pyspark.sql.SparkSession.builder.master('''local[*]'''		).appName('''pyspark'''		).getOrCreate()
			UpperCAmelCase_      :       List[str]          =       spark.range(1_0_0		).repartition(1		)
			UpperCAmelCase_      :       List[Any]          =       Spark(A		)
			# The id ints will be converted to Pyarrow int64s, so each row will be 8 bytes. Setting a max_shard_size of 16 means
			# that each partition can hold 2 rows.
			spark_builder._repartition_df_if_needed(max_shard_size=1_6		)
			# Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
			assert spark_builder.df.rdd.getNumPartitions() == 5_0
@require_not_windows
@require_dill_gt_0_3_2
def __UpperCAmelCase   (					)    ->   str:
			UpperCAmelCase_      :       List[str]          =       pyspark.sql.SparkSession.builder.master('''local[*]'''		).appName('''pyspark'''		).getOrCreate()
			UpperCAmelCase_      :       Optional[Any]          =       spark.range(1_0		).repartition(2		)
			UpperCAmelCase_      :       int          =       [1, 0]
			UpperCAmelCase_      :       str          =       _generate_iterable_examples(A			,       A		)  # Reverse the partitions.
			UpperCAmelCase_      :       Optional[Any]          =       _get_expected_row_ids_and_row_dicts_for_partition_order(A			,       A		)

			for i, (row_id, row_dict) in enumerate(generate_fn()		):
						UpperCAmelCase_    ,      UpperCAmelCase_      :       int          =       expected_row_ids_and_row_dicts[i]
						assert row_id == expected_row_id
						assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def __UpperCAmelCase   (					)    ->   Union[str, Any]:
			UpperCAmelCase_      :       str          =       pyspark.sql.SparkSession.builder.master('''local[*]'''		).appName('''pyspark'''		).getOrCreate()
			UpperCAmelCase_      :       List[Any]          =       spark.range(1_0		).repartition(1		)
			UpperCAmelCase_      :       Optional[int]          =       SparkExamplesIterable(A		)
			assert it.n_shards == 1
			for i, (row_id, row_dict) in enumerate(A		):
						assert row_id == F"0_{i}"
						assert row_dict == {"id": i}
@require_not_windows
@require_dill_gt_0_3_2
def __UpperCAmelCase   (					)    ->   Union[str, Any]:
			UpperCAmelCase_      :       Dict          =       pyspark.sql.SparkSession.builder.master('''local[*]'''		).appName('''pyspark'''		).getOrCreate()
			UpperCAmelCase_      :       Dict          =       spark.range(3_0		).repartition(3		)
			# Mock the generator so that shuffle reverses the partition indices.
			with patch('''numpy.random.Generator'''		) as generator_mock:
						UpperCAmelCase_      :       Any          =       lambda A		: x.reverse()
						UpperCAmelCase_      :       Dict          =       _get_expected_row_ids_and_row_dicts_for_partition_order(A			,       [2, 1, 0]		)

						UpperCAmelCase_      :       List[Any]          =       SparkExamplesIterable(A		).shuffle_data_sources(A		)
						assert shuffled_it.n_shards == 3
						for i, (row_id, row_dict) in enumerate(A		):
									UpperCAmelCase_    ,      UpperCAmelCase_      :       Dict          =       expected_row_ids_and_row_dicts[i]
									assert row_id == expected_row_id
									assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def __UpperCAmelCase   (					)    ->   int:
			UpperCAmelCase_      :       Tuple          =       pyspark.sql.SparkSession.builder.master('''local[*]'''		).appName('''pyspark'''		).getOrCreate()
			UpperCAmelCase_      :       int          =       spark.range(2_0		).repartition(4		)

			# Partitions 0 and 2
			UpperCAmelCase_      :       int          =       SparkExamplesIterable(A		).shard_data_sources(worker_id=0			,       num_workers=2		)
			assert shard_it_a.n_shards == 2
			UpperCAmelCase_      :       Union[str, Any]          =       _get_expected_row_ids_and_row_dicts_for_partition_order(A			,       [0, 2]		)
			for i, (row_id, row_dict) in enumerate(A		):
						UpperCAmelCase_    ,      UpperCAmelCase_      :       Dict          =       expected_row_ids_and_row_dicts_a[i]
						assert row_id == expected_row_id
						assert row_dict == expected_row_dict

			# Partitions 1 and 3
			UpperCAmelCase_      :       Tuple          =       SparkExamplesIterable(A		).shard_data_sources(worker_id=1			,       num_workers=2		)
			assert shard_it_a.n_shards == 2
			UpperCAmelCase_      :       List[str]          =       _get_expected_row_ids_and_row_dicts_for_partition_order(A			,       [1, 3]		)
			for i, (row_id, row_dict) in enumerate(A		):
						UpperCAmelCase_    ,      UpperCAmelCase_      :       Union[str, Any]          =       expected_row_ids_and_row_dicts_a[i]
						assert row_id == expected_row_id
						assert row_dict == expected_row_dict







@require_not_windows
@require_dill_gt_0_3_2
def __UpperCAmelCase   (					)    ->   Any:
			UpperCAmelCase_      :       List[Any]          =       pyspark.sql.SparkSession.builder.master('''local[*]'''		).appName('''pyspark'''		).getOrCreate()
			UpperCAmelCase_      :       List[str]          =       spark.range(1_0_0		).repartition(1		)
			UpperCAmelCase_      :       int          =       Spark(A		)
			# Choose a small max_shard_size for maximum partitioning.
			spark_builder._repartition_df_if_needed(max_shard_size=1		)
			# The new number of partitions should not be greater than the number of rows.
			assert spark_builder.df.rdd.getNumPartitions() == 1_0_0


